https://users.fmrib.ox.ac.uk/~jesper/papers/readgroup_071009/Ashburner07.pdf

A fast diffeomorphic image registration algorithm

In 1997, Azuma published a survey on augmented reality (AR). Our goal is to complement, rather than replace, the original survey by presenting representative examples of the new advances. We refer one to the original survey for descriptions of potential applications (such as medical visualization, maintenance and repair of complex equipment, annotation, and path planning); summaries of AR system characteristics (such as the advantages and disadvantages of optical and video approaches to blending virtual and real, problems in display focus and contrast, and system portability); and an introduction to the crucial problem of registration, including sources of registration error and error-reduction strategies.

https://www.cc.gatech.edu/~blair/papers/ARsurveyCGA.pdf

Recent advances in augmented reality

https://www-sop.inria.fr/asclepios/Publications/Tom.Vercauteren/Eval14NonlinearRegistration-Klein-NeuroImage09.pdf

Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration.

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe.

/pdf/histopathological-image-analysis-a-review-40as3fmhah.pdf

Histopathological Image Analysis: A Review

Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: 1) multi-modality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; 2) longitudinal studies, where temporal structural or anatomical changes are investigated; and 3) population modeling and statistical atlases used to study normal anatomical variability. In this paper, we attempt to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain. Additional emphasis has been given to techniques applied to medical images. In order to study image registration methods in depth, their main components are identified and studied independently. The most recent techniques are presented in a systematic fashion. The contribution of this paper is to provide an extensive account of registration techniques in a systematic manner.

/pdf/deformable-medical-image-registration-a-survey-2uee59wp1z.pdf

Deformable Medical Image Registration: A Survey

Medical image computing and computer-assisted intervention - MICCAI '98 : first International Conference, Cambridge, MA, USA, October 11-13, 1998 : proceedings

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Medical image computing and computer-assisted intervention : MICCAI 2000, Third International Conference, Pittsburgh, PA, USA, October 11-14, 2000 : proceedings

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Medical image computing and computer-assisted intervention - MICCAI '98 : first International Conference, Cambridge, MA, USA, October 11-13, 1998 : proceedings

Medical image computing and computer-assisted intervention : MICCAI 2000, Third International Conference, Pittsburgh, PA, USA, October 11-14, 2000 : proceedings